Computer-based systems configured for caller identification differentiation and methods of use thereof

ABSTRACT

Systems and methods of caller identification differentiation via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may include: receiving a permission indicator identifying a permission by the user to detect calls being received by a computing device; receiving an indication of a current call being received; utilizing a trained call differentiation machine learning model to determine a likelihood that the current call is of a first call type or a second call type, where the first call type is associated with a first type of activity and the second call type is associated with a second type of activity.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in drawings that form a part of this document:Copyright, Capital One Services, LLC., All Rights Reserved.

FIELD OF TECHNOLOGY

The present disclosure generally relates to improvedcomputer-implemented methods, improved computer-based platforms orsystems, improved computing components and devices configured for one ormore practical technological improvement applications utilizing one ormore machine learning techniques to curate additional caller informationto enhance caller line identification information (e.g., caller ID),including, but not limited to, augmenting caller ID with informationrelated to differentiated caller identification.

BACKGROUND OF TECHNOLOGY

A computer network platform/system may include a group of computers(e.g., clients, servers, computing clusters, cloud resources, etc.) andother computing hardware devices that are linked and communicate viasoftware architecture, communication applications, and/or softwareapplications associated with electronic transactions, data processing,and/or service management. For example, without limitation, oneexemplary technological problem exists when legitimately-purposed callsfrom unknown phone numbers may not be distinguished from spam based onactivity patterns specific to the calling parties. Yet another exemplarytechnological problem may exist when calling party informationdynamically procured from various sources according to calleridentification is not sufficient to differentiate from spammers.

SUMMARY OF DESCRIBED SUBJECT MATTER

In some embodiments, the present disclosure provides various exemplarytechnically improved computer-implemented methods involving calleridentification differentiation, the method including steps such as:obtaining, by one or more processors, a trained call differentiationmachine learning model that determines a likelihood that a particularcall associated with a particular phone number is of a first call typeor a second call type, where the first call type is associated with afirst activity and the second call type is associated with a secondactivity; receiving, by the one or more processors, from a computingdevice of a first user, a permission indicator identifying a permissionby the first user to detect calls being received by the computingdevice; receiving, by the one or more processors, from the computingdevice, an indication of a current call being received at a current timefrom a particular phone number that is associated with a second user;utilizing, by the one or more processors, the trained calldifferentiation machine learning model to: determine an activity typeassociated with the second user, determining an activity qualifierassociated with the second user based at least in part on the activitytype, and determine the likelihood that the current call is of the firstcall type or the second call type, based at least in part on the currenttime and the activity qualifier; and instructing, by the one or moreprocessors, when the current call is of the first call type, thecomputing device of the first user to present to the first user, agraphical user interface (GUI) associated with the current call, wherethe GUI includes at least one GUI element, displaying, to the firstuser, information related to the first type of activity of the seconduser.

In some embodiments, the present disclosure also provides exemplarytechnically improved computer-based systems, and computer-readablemedia, including computer-readable media implemented with and/orinvolving one or more software applications, whether resident onpersonal transacting devices, computer devices or platforms, providedfor download via a server and/or executed in connection with at leastone network and/or connection, that include or involve features,functionality, computing components and/or steps consistent with thoseset forth herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the present disclosure can be further explainedwith reference to the attached drawings, where like structures arereferred to by like numerals throughout the several views. The drawingsshown are not necessarily to scale, with emphasis instead generallybeing placed upon illustrating the principles of the present disclosure.Therefore, specific structural and functional details disclosed hereinare not to be interpreted as limiting, but merely as a representativebasis for teaching one skilled in the art to variously employ one ormore illustrative embodiments.

FIG. 1 is a block diagram of an exemplary system and/or platformillustrating aspects of caller identification differentiation,consistent with exemplary aspects of certain embodiments of the presentdisclosure.

FIG. 2 is a diagram illustrating an exemplary process involving aspectsand features associated with caller identification differentiation,consistent with exemplary aspects of certain embodiments of the presentdisclosure.

FIGS. 3A-3C are diagrams illustrating exemplary graphical userinterfaces (GUIs) involving aspects and features associated with calleridentification differentiation, consistent with exemplary aspects ofcertain embodiments of the present disclosure.

FIG. 4 is a flowchart illustrating an exemplary process related tocaller identification differentiation, consistent with exemplary aspectsof certain embodiments of the present disclosure.

FIG. 5 is a block diagram depicting an exemplary computer-based system,in accordance with certain embodiments of the present disclosure.

FIG. 6 is a block diagram depicting another exemplary computer-basedsystem, in accordance with certain embodiments of the presentdisclosure.

FIGS. 7 and 8 are diagrams illustrating two exemplary implementations ofcloud computing architecture/aspects with respect to which the disclosedtechnology may be specifically configured to operate, in accordance withcertain embodiments of the present disclosure.

DETAILED DESCRIPTION

Various detailed embodiments of the present disclosure, taken inconjunction with the accompanying figures, are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely illustrative. In addition, each of the examples given inconnection with the various embodiments of the present disclosure isintended to be illustrative, and not restrictive.

Throughout the specification, the following terms take the meaningsexplicitly associated herein, unless the context clearly dictatesotherwise. The phrases “in one embodiment” and “in some embodiments” asused herein do not necessarily refer to the same embodiment(s), thoughit may. Furthermore, the phrases “in another embodiment” and “in someother embodiments” as used herein do not necessarily refer to adifferent embodiment, although it may. Thus, as described below, variousembodiments may be readily combined, without departing from the scope orspirit of the present disclosure.

To benefit from the diversity of and intelligence gleaned from variouscommunication events and data sources, while at the same time toleverage advanced data processing capabilities, various embodiments ofthe present disclosure provide for improved computer-based platforms orsystems, improved computing components and devices configured for one ormore practical technological improvements involving: detecting phonecalls received at computing devices of users, determining likelihoodsthat current calls are of a particular call type differentiated fromspam, procuring additional information for the calling parties engagingthe particular call type from various sources, displaying the procuredadditional information in a GUI associated with the current calls, aswell as generating intelligence (e.g., trained call differentiationmachine learning models, etc.) empowered by the various phone callrelated events, calling party profile information, contextualinformation, and/or transaction data, and/or call receiving partyprofile information, contextual information, and/or transaction data to,for example, automate the functionality of spam-differentiating forcalling party's identification with enhanced efficiency, accuracy,relevancy, and accessibility. For example, without limitation, oneexemplary technological problem may be addressed by exemplarytechnological solutions provided by some embodiments of the presentdisclosure by distinguishing legitimately-purposed calls associated withunknown phone numbers from spam based on activity patterns specific tothe calling parties. Yet another exemplary technological problem may beaddressed by exemplary technological solutions provided by someembodiments of the present disclosure by dynamically procuringinformation about calling party(ies) from various sources according tocaller identification sufficiently differentiated from spammers inreal-time.

Various embodiments disclosed herein may be implemented in connectionwith one or more entities that provide, maintain, manage, and/orotherwise offer any services involving various transaction data and/orcommunication(s). In some embodiments, the exemplary entity may be afinancial service entity that provides, maintains, manages, and/orotherwise offers financial services. Such financial service entity maybe a bank, credit card issuer, or any other type of financial serviceentity that generates, provides, manages, and/or maintains financialservice accounts that entail providing a transaction card to one or morecustomers, the transaction card configured for use at a transactingterminal to access an associated financial service account. In someembodiments, financial service accounts may include, for example, creditcard accounts, bank accounts such as checking and/or savings accounts,reward or loyalty program accounts, debit account, and/or any other typeof financial service account.

For purposes of illustration, data structures and/or operations specificto phone calls may be used herein as non-limiting examples to describesome embodiments of the present disclosure. Various aspects of variousdisclosed technological improvements may apply to communications atvarious modalities. For example, the curated caller information may beused to augment record(s) associated with identification(s) of a user's(e.g., communication initiating small business owner's), for example,email address, chatting account, social media account, and so on.Correspondingly, the curated additional caller information may bedynamically displayed to another user in receipt of an incomingcommunication initiated by the user via various communicationmodalities. By way of non-limiting examples, such communications may bein the forms of an SMS, an MMS, an email, a voice message, a chattingmessage, a social media message, a push message of an application, andthe like, not limited by the embodiments illustrated herein.

FIG. 1 depicts an exemplary computer-based system 100 illustratingaspects of technologically improved caller identificationdifferentiation via utilization of at least one machine learningtechnique, in accordance with one or more embodiments of the presentdisclosure. An exemplary system 100 may include at least one server 101,and at least one first computing device 150 associated with a firstuser, which may communicate 103 over at least one communication network105. In some embodiments and in optional combination with one or moreembodiments described herein, the system 100 may further include and/orbe operatively connected and/or be in communication (e.g., electroniccommunication, telecommunication) with at least one second computingdevice 180 associated with a second user, the second computing device180 may also communicate with via the communication network 105 to, forexample, receive phone call(s), SMS message(s), MMS message(s), chatapplication message(s), social media message(s), and the like, from thefirst computing device 150.

In some embodiments, server 101 may include computers, servers,mainframe computers, desktop computers, etc. configured to executeinstructions to perform server and/or client-based operations that areconsistent with one or more aspects of the present disclosure. In someembodiments, server 101 may include a single server, a cluster ofservers, or one or more servers located in local and/or remotelocations. In some embodiments, server 101 may be standalone, or it maybe part of a subsystem, which may, in turn, be part of a larger computersystem. In some embodiments, server 101 may be associated with an entitysuch as a financial institution, such as a credit card company thatservices an account of the user, and thereby having access totransactions performed by various users in addition to their respectiveaccount information. For example, the second user may incur atransaction with the first user, using a transaction card issued by thecredit card company, with a computing device of a merchant (not shownherein) either online or at a point of sale (POS) device of the merchantvia placing an order for a good, a service, or some combination thereof.As illustrated with more details below, in some embodiments, the firstuser may operate as a small business owner (e.g., a sole proprietor) andutilize the same phone number for conducting both business calls andpersonal calls.

Still referring to FIG. 1 , server 101 may include at least oneprocessor 102, and a non-transient memory 104, such as random-accessmemory (RAM). In some embodiments, memory 104 may store application(s)and data 108. Various embodiments herein may be configured such that theapplication(s) and data 108, when executed by the processor 102, mayutilize one or more machine learning techniques to provide all orportions of the features and/or functionality associated with calleridentification differentiation, in conjunction with or independent ofcaller identification differentiation functionality implemented at thefirst computing device 150 and/or the second computing device 180.

In some embodiments, the features and functionality may includeoperations such as: obtaining training data (e.g., training informationof a plurality of users (e.g., small business owners), training activityinformation associated with a plurality of activities associated withthe plurality of users, training phone number information of a pluralityof phone numbers associated with a plurality of calls (of at least afirst call type or a second call type) from the plurality of users to aplurality of call receiving users, training timing informationassociated with the plurality of users engaging the plurality ofactivities and/or initiating the plurality of calls, and/or trainingprofile information, contextual information, and/or transaction dataassociated with the plurality of users and/or call receiving users);obtaining a trained call differentiation machine learning model thatdetermines a likelihood that a particular call associated with aparticular phone number is of a first call type or a second call type,where the first call type is associated with a first type of activityand the second call type is associated with a second type of activity;receiving an indicator identifying a permission by the second user todetect phone calls, emails, messages, and/or other communicationsreceived at the second computing device of the second user; receiving anindication that at least one communication is received by the seconduser from the first user; utilizing the trained call differentiationmachine learning model to determine: 1) an activity type associated withthe first user, 2) an activity qualifier associated with the first userbased at least in part on the activity type, and 3) the likelihood thatthe current call is of the first call type or the second call type,based at least in part on the current time and the activity qualifier;and instructing the second computing device to, when the current call isof the first call type, display information related to the first type ofactivity of the first user to the second user. In some embodiments notshown herein, the features and functionality of the server 101 may bepartially or fully implemented at the first computing device 150 and/orthe second computing device 180 such that the illustrative process toprovide caller identification differentiation may be performed partiallyor entirely on the second computing device 180.

In some embodiments, the application(s) and data 108 may include anexemplary trained call differentiation machine learning model 122. Insome embodiments, the trained call differentiation machine learningmodel 122 may be trained utilizing the server 101 and/or utilizing atleast one remote processor. In other embodiments, the trained calldifferentiation machine learning model 122 may be obtained when aninitial call differentiation machine learning model would have beentrained by another entity with the training data provided by anotherentity, and/or with the training data provided by server 101, to obtainthe trained call differentiation machine learning model 122. In someembodiments, the first computing device 150 and/or the second computingdevice 180 may be utilized to train the initial call differentiationmachine learning model and/or re-train the trained call differentiationmachine learning model 122. In the latter case, the trained calldifferentiation machine learning model 122 may be trained and/orre-trained with training data specific to the first user and/or thesecond user at their respective computing devices. In one example, thetrained call differentiation machine learning model 122 may be trainedas a user-specific (e.g., merchant-specific) machine learning model.

Various machine learning techniques may be applied to train and re-trainthe initial and trained call differentiation machine learning model withtraining data and feedback data, respectively. In some embodiments, thetraining data may include various information related to transactionevents and communication events. By way of non-limiting examples, thetraining data may include transaction dates, transaction time, timelapses between transactions relative to business hours (e.g., learned orotherwise obtained), time lapses since transactions occurred, and thelike. In various implementations, such a machine learning process may besupervised, unsupervised, or a combination thereof. In some embodiments,such a machine learning model may include a statistical model, amathematical model, a Bayesian dependency model, a naive Bayesianclassifier, a Support Vector Machine (SVMs), a neural network (NN),and/or a Hidden Markov Model.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, an exemplary neutral network technique may beone of, without limitation, feedforward neural network, radial basisfunction network, recurrent neural network, convolutioal network (e.g.,U-net) or other suitable network. In some embodiments and, optionally,in combination of any embodiment described above or below, an exemplaryimplementation of neural network may be executed as follows:

-   -   a. Define Neural Network architecture/model,    -   b. Transfer the input data to the exemplary neural network        model,    -   c. Train the exemplary model incrementally,    -   d. determine the accuracy for a specific number of timesteps,    -   e. apply the exemplary trained model to process the        newly-received input data,    -   f. optionally and in parallel, continue to train the exemplary        trained model with a predetermined periodicity.

In some embodiments and, optionally, in combination with any embodimentdescribed above or below, the exemplary trained call differentiationmachine learning model 122 may be in the form of a neural network,having at least a neural network topology, a series of activationfunctions, and connection weights. For example, the topology of a neuralnetwork may include a configuration of nodes of the neural network andconnections between such nodes. In some embodiments and, optionally, incombination with any embodiment described above or below, the exemplarytrained neural network model may also be specified to include otherparameters, including but not limited to, bias values/functions and/oraggregation functions. For example, an activation function of a node maybe a step function, sine function, continuous or piecewise linearfunction, sigmoid function, hyperbolic tangent function, or other typeof mathematical function that represents a threshold at which the nodeis activated. In some embodiments and, optionally, in combination of anyembodiment described above or below, the exemplary aggregation functionmay be a mathematical function that combines (e.g., sum, product, etc.)input signals to the node. In some embodiments and, optionally, incombination of any embodiment described above or below, an output of theexemplary aggregation function may be used as input to the exemplaryactivation function. In some embodiments and, optionally, in combinationof any embodiment described above or below, the bias may be a constantvalue or function that may be used by the aggregation function and/orthe activation function to make the node more or less likely to beactivated.

In some embodiments, the application(s) and data 108 may include a calldifferentiation engine 124 that may be programmed to execute theexemplary trained call differentiation machine learning model 122. Insome embodiments, the call differentiation engine 124 may receive, asinput, an indication of a call incoming at a particular time from aphone number associated with the first user yet unknown to the seconduser. In some embodiments, the trained call differentiation machinelearning model 122 may receive input of various other information suchas, but not limited to, transaction dates, transaction time, time lapsesbetween transactions relative to business hours (e.g., learned orotherwise obtained), time lapses since the last transaction(s) occurred,and the like. As an output, the call differentiation engine 124 mayutilize the trained call differentiation machine learning model 122 todetermine a likelihood that a particular call associated with the phonenumber is of a particular call type (e.g., a first call type or a secondcall type, the first call type associated with a first type of activityand the second call type associated with a second type of activity) foraugmenting the caller ID information associated with the call. In someembodiments, the likelihood information may be used to procureadditional information with regard to the type of activities of thefirst user. In one example, when the particular call is determined as ofa first call type (e.g., a business call type), additional informationof the business operations of the first user may be obtained and used toaugment the caller ID information associated with the particular call,in addition to or separately from notifying to the second user of theparticular call not being spam. More details of the procurement of suchadditional information of the activities of the first type andnotification to the second user are described with reference to FIGS. 2,and 3A-3C, below.

Still referring to FIG. 1 , an illustrative second computing device 180associated with the second user may include: one or more processors 181and a non-transient computer memory 182. Memory 182 may storeinstructions that, when executed by the one or more processors 181,perform various procedures, operations, or processes consistent withdisclosed embodiments. In one embodiment, the memory 182 may include anapplication (APP) 194 that, when executed by the one or more processors181, may perform operations such as: prompting the second user for apermission to detect communications via the second computing device 180(e.g., phone calls, SMS, emails, etc.); detecting communications via thesecond computing device 180 according to the permission obtained fromthe second user; receiving, and/or storing differentiated callerinformation 198; and triggering the second computing device 180 intodisplaying the differentiated caller information as part of the callerID information associated with the phone number of the first user, tothe second user, upon detecting a call incoming from such a phonenumber. By way of non-limiting examples, the differentiated callerinformation 198 may include information such as an activity type of thefirst user associated with a timing condition, or information related tothe activities of the first activity type of the first user (e.g., atitle related to the first type of activity, an address related to thefirst type of activity, or a description of the first type ofactivities, and so on). In one example, when the activity type isrelated to the first user operating a business thereof, the informationmay include a business name, an address of the business, business hours,contacts (e.g., a web link of the business, a social media link of thebusiness, a messaging link, a reservation link, other phone number(s),fax number(s)), photo(s), customer review(s), promotional information,description of the business (e.g., lunch menu, dinner menu), surveyinformation, and so on.

In various embodiments, the application 194 may be implemented in anysuitable manner such as, without limitation, a standalone application, abrowser extension, and the like. Various features and functionality ofthe application 194 may be implemented as part of other applications,and/or implemented in multiple applications to include more than, lessthan the features and functionality described above, and/or withcombination with any other features and functionality.

In some embodiments, the application 194 may be configured such that thefunctionality involving phone calls (and communications at various othercommunication channels) is implemented in a separate applicationexecuting on the second computing device 180. For instance, such anapplication may be configured to detect a variety of communications ofthe second user at the second computing device 180, intercept thosecommunications at the second computing device 180, monitor thosecommunications at the second computing device 180, and/or interject oneor more suitable controls (e.g., user operable controls) over thosecommunications at the second computing device 180. In some embodiments,such an application may be configured to obtain information of thecommunication transmitting party(ies) (e.g., the calling phone number),information of the communication (e.g., the calling time of a phonecall), the content of the communication (e.g., the identity of an SMSsending entity, the content of the conversation of a phone call). Insome embodiments, such an application may be configured to obtainpermissions from the second user in order to execute all or part of theexemplary functionality described above. In some embodiments, theapplication 194 may be configured to execute on the first computingdevice 150 as well.

In some embodiments, for the purpose of simplicity, features andfunctionalities associated with the exemplary trained calldifferentiation machine learning model 122 (e.g., training, re-training,etc.) are illustrated as implemented by components of server 101. Itshould be noted that one more of those call differentiation machinelearning model-related aspects and/or features may be implemented at orin conjunction with the second computing device 180 of the second user.For example, in some embodiments, the initial call differentiationmachine learning model may be partially trained at the server 101 with,for example, other users' information (e.g., activity information,transaction information, etc.) and phone call events, then in turntransmitted to the second computing device 180 (and/or the firstcomputing device 150) to be fully trained with, for example, the seconduser (and/or the first user) specific information and phone call events.In another example, the converse may be performed such that the initialmachine learning model may be initially trained at the second computingdevice 180 (and/or the first computing device 150) and subsequentlytransmitted to the server 101 for application and/or further trainingwith training data from other users.

Further, the local differentiated caller information 198 may also bestored entirely on the second computing device 180, in conjunction withthe server 101, or entirely at server 101. In some embodiments, when thecall differentiation machine learning model is trained or re-trained atthe second computing device 180 (and/or the first computing device 150),the trained call differentiation machine learning model 122 may beutilized to, at least with regard to the activity type of the firstuser, generate or update the activity type and/or additional informationassociated with the activity type of the first user, locally or inconjunction with the server 101. In an embodiment, the first computingdevice 150 and/or second computing device 180 may be configured tosynchronize a local collection of differentiated caller information tothe server 101 for storage and/or access by computing devices of otherusers. In some implementations, the synchronization may be performed inany suitable manner, such as, for example, in a pushing manner initiatedby the first computing device 150/the second computing device 180, in apulling manner initiated by the server 101, or in combination thereof.

Various embodiments associated with FIG. 1 and related disclosure hereinsolve a technical problem of differentiating calls from unknown phonenumbers as non-spam based on activity patterns specific to callingparties. Aspects of the disclosed procurement of additional informationrelated to activities of the calling parties via dynamic sourcing alsoyield more accurate, informative, efficient and otherwise improvedutilization of both processing and communication resources, such as vialeveraging the exclusive, private access to comprehensive user data(both training and real user communication events, transaction events,feedback events, etc.) to train and re-train a machine learning model,using the trained machine learning model to determine at least one of anactivity type associated with a calling party, an activity qualifierassociated with the calling party, an activity type associated with acall receiving party such that to discern a likelihood that a currentcall of the calling party from a phone number unknown to the callreceiving party is of a call type sufficiently differentiated from spam(e.g., with a likelihood score). As a result, the call receiving partycan be guarded against true spam imposing telecommunication securityissues in an improved manner that is no longer a blanket applicationlabeling all calls from unknown phone numbers as spam. Rather, exemplarytechnological improvement disclosed herein enables the filtering oflegitimate communications by leveraging machine learning model poweredanalysis of profile information, contextual information, as well astransactional information of the communication parties. Moreover,various exemplary embodiments enabled by the disclosed procurement ofadditional information related to activities of calling parties mayallow for improved responsiveness, efficiency, accuracy, and expandedaccessibility in terms of providing data informative of the particularrole of the calling party, thereby increasing telecommunication securityand/or reducing or eliminating the need for unnecessary processingcaused by privacy-invading if not fraudulent actions otherwise avoidedby the present differentiated caller information procurement mechanisms.

While only one server 101, first computing device 150, network 105, andsecond computing device 180 are shown, it will be understood that system100 may include more than one of any of these components. Moregenerally, the components and arrangement of the components included insystem 100 may vary. Thus, system 100 may include other components thatperform or assist in the performance of one or more processes consistentwith the disclosed embodiments. For instance, in some embodiments, thefeature and functionality of the server 101 may be partially, or fullyimplemented at the first computing device 150 and/or the secondcomputing device 180.

FIG. 2 is a diagram illustrating an exemplary differentiation of calleridentification using one or more machine learning techniques, consistentwith exemplary aspects of certain embodiments of the present disclosure.In this illustrated embodiment, an exemplary caller identificationdifferentiation system providing exemplary time-sensitive and/ortransaction-sensitive caller identification information for calls placedby (202) a merchant (e.g., small business owner, the first user of FIG.1 , etc.) and received at a device of a recipient (e.g., a customer ofthe merchant, the second user of FIG. 1 , etc.). Notably, it tends to bea common practice for a small business owner to use the same phonenumber to conduct both business calls and personal calls. In thisexample, the phone number may be registered (204) by the merchant orotherwise indicated/established (e.g., in Yelp, Postmates, DoorDash,etc.) as associated with a business thereof, and thus associated withthe merchant. On the other hand, despite being associated with themerchant (or the merchant's business and the like), the phone number maystill remain unknown to the call recipient. For example, via the currenttechnologies, when the call recipient and the merchant have communicatedin the past (e.g., the call recipient may have called several mechanicsnearby to fix a car problem, or eateries nearby to order food deliveriesor pickups, etc.), absent the call recipient's affirmative actions tostore the merchant's phone number in a phonebook of contacts residing onthe device thereof, the phone number of the merchant may still beindicated as unknown or spam risk in association with calls placetherefrom.

In the illustrative embodiment shown in FIG. 2 , the exemplary calleridentification differentiation system may be configured to utilize atrained call differentiation machine learning model to determine (206)whether the merchant is open for business at the particular point of thetime when the call is initiated at step 202. Details with regard to thetrained call differentiation model are similar to those described abovewith reference to FIG. 1 , and are not repeated. In some embodiments,and as illustrated in this example, upon determining that the merchantis open for business at step 206, the exemplary caller identificationdifferentiation system may instruct the device of the recipient toupdate (208) a call screen thereof such that the call is not indicatedas spam or unknown (but rather a call differentiated from spam). In someimplementations, the merchant information may be displayed to therecipient as well. By way of non-limiting examples, the merchantinformation may be rendered and/or displayed to the recipient as part ofthe caller ID information as illustrated with reference to FIGS. 3B-3C,below. In other embodiments, the merchant information may be renderedand/or displayed to the recipient in any suitable form or format, suchas and not limited to, an audio message, a tone, a push notification, ananimated notification, a user-configured notification form/format, andthe like.

In some embodiments, the merchant information may include anyinformation pertaining to the merchant and/or merchant's business. Byway of non-limiting examples, the merchant information may include oneor more of: the merchant's name, business name, business address,business hours, website of the business, social media sites/accountsassociated with the merchant/business, a messaging link, a reservationlink, other phone number(s), fax number(s), photo(s), customerreview(s), promotional information, description of the business (e.g.,lunch menu, dinner menu), survey information, and so on. Variousembodiments herein may be configured such that the merchant informationcan by dynamically sourced from various data reservoirs or informationhosting platforms. For example, the merchant information may be procuredfrom the merchant's Yelp website, FourSquare website, social mediawebsite, and the like. In some embodiments, the merchant information maybe crowdsourced from various users (e.g., customers of the merchant) andstored at the exemplary caller identification differentiation system inassociation with the phone number of the merchant/identification of themerchant. In one embodiment, the merchant information may be stored aspart of the differentiated caller information at the server, and/or thedevice of the recipient (e.g., the differentiated caller information198).

In some embodiments, and as illustrated in this example, upondetermining that the merchant is not open for business at step 206, theexemplary caller identification differentiation system may furtherdetermine (210) whether the merchant is conducting a business call atstep 202 based on the merchant's historical transactions. According tosome aspects of the present disclosure, the exemplary calleridentification differentiation system may utilize the trained calldifferentiation machine learning model 122 to perform a secondaryprediction (e.g., secondary verification/adjustment) with regard to themerchant's capacity associated with the call initiated in step 202, moredetails of which are described with reference to FIG. 4 , below.

In some embodiments, when in the secondary verification using themerchant's historical transactions indicates that the merchant isconducting a business call at step 202, the exemplary calleridentification differentiation system may similarly instruct the deviceof the recipient to update (208) a call screen thereof such that anindication of non-spam and/or the merchant information may be displayedto the recipient.

In other embodiments, when in the secondary verification using themerchant's historical transactions indicates that the merchant is notconducting a business call at step 202, the exemplary calleridentification differentiation system may instruct the device of therecipient not to update (212) a call screen thereof. In some embodimentsand as illustrated with reference to FIG. 3A below, the caller IDinformation displayed to the recipient may default to the informationconfigured or available for representation to the recipient uponreceiving calls from unknown numbers (e.g., unknown call or spam risk).

Various embodiments herein may be configured such that steps 206 and 210can be performed in a different order. For example, the determinationwith regard to the merchant's calling capacity may be performed based onthe merchant's historical transactions (and/or other transactional datainvolving the merchant and the recipient) prior to determining whetherthe calling time falls within predicted business hours of the merchant.

FIGS. 3A-3C are diagrams illustrating exemplary graphical userinterfaces (GUIs) involving aspects associated with calleridentification differentiation, consistent with exemplary aspects ofcertain embodiments of the present disclosure. In some embodiments, theGUIs may be provided by an illustrative application (e.g., theapplication 194 executing on the second computing device 180) and shownon a display of a mobile device (e.g., the second computing device 180)of a user.

In some embodiments, and as shown in FIGS. 3A-3C, the illustrativeapplication 194 may be configured to display the differentiated calleridentification information (e.g., role-specific caller information,call-type-specific caller information, etc.) to the user as part of thecaller ID information (e.g., an enhanced GUI for representing callerID). For instance, the application 194 may be configured to make an APIrequest (e.g., a push call) to the phone interface application (e.g.,the native phone application configured to display conventional callerID information on the display of the mobile device) to pass theinformation of the differentiated caller identification information, forexample, as a parameter to the phone interface application. As a result,upon receiving the information of the differentiated calleridentification information, the phone interface application may beconfigured to display the information such as the differentiated calleridentification information on the display of the mobile device.

Any suitable techniques may be implemented to represent and notify tothe user of the differentiated caller identification information, notlimited by the embodiments illustrated herein. By way of non-limitingexamples, the differentiated caller identification information may beprovided to the user using media such as an audio message, a graphicaldisplay (e.g., a banner, a floating window overlaying the GUI of thenative phone interface application, etc.), a push notification, atextual display at the GUI elements of a home screen of the computingdevices, and so on.

FIG. 3A illustrates an exemplary GUI 301 of the illustrative application(e.g., the application 194) for displaying caller ID information to theuser upon a call incoming at the mobile device. The GUI 301 may includea caller ID 302, and a set of buttons 303, 305, and 308 for the user toselect. Here, the caller ID 302 may be configured to display to the userthat the call is from a calling party associated with a calling phonenumber, i.e., “1-347-000-0000,” and “New York” as the area associatedwith the phone number's area code.

In this illustrative embodiment, the phone number may have beendetermined as not associated with a call type differentiated from spam,or, as described above with reference to FIG. 2, not associated with abusiness call placed within the predicted caller's (e.g., merchant's)business hours or a business call predicted based on the caller's (e.g.,merchant's) historical transactions. Thus, the application 194 may notfurther obtain any additional information of the caller (e.g.,merchant), absent any notification of the triggering condition of thecall being differentiated from spam calls. That is, when notdifferentiated as non-spam, the call from the phone number unknown tothe user and incoming at the mobile device may be represented as, forexample, spam risk or unknown, to alert the user accordingly. As aresult, at the GUI 301, the caller ID information may not be augmentedand hence displayed without any additional caller information. Forexample, using the native caller ID feature of the mobile device, thecaller ID information may be determined as “Unknown Caller” based onthat the particular phone number “1-347-000-0000” is unknown to theuser. As such, here in FIG. 3A, the GUI 301 may be configured to displaythe caller ID 302 as “Unknown Caller from 1-347-000-0000 New York.”

Here, at GUI 301, the user can interact with the selectable options toperform actions with regard to the pending incoming call. In thisexample, the user can select the button 303 to screen the incoming call,select the button 305 to reply with a message, or select the button 308to swipe up to answer the incoming call. The incoming call can bescreened by various techniques to evaluate the characteristics of thecalling entity. Exemplary screening techniques may include the userscreening a message being recorded on an answering machine or voicemail, the user checking a caller ID display to see who or where the callis from, and the user checking the time or date which a call or messagewas received. Exemplary screening techniques may also include connectingthe calling party to a chatbot service such that the chatbot service mayscreen the calling party and/or record the conversion. Inimplementations, screening may be performed by protocols such as SecureTelephony Identity Revisited (STIR), Signature-based Handling ofAsserted information using toKENs (SHAKEN) to identify calls associatedwith spoofing phone numbers, and the like.

Further, the user may perform other actions upon the incoming call inaddition to or in place of those illustrated in FIG. 3A. For example,the user can interact with the GUI 301 to decline the incoming callwhile it is still pending, report the phone number included in thecaller ID 302 to a server (e.g., the server 101 of FIG. 1 ) or loglocally as associated with a business call from the caller withoutscreening the call or after screening the call, report the phone numberor log locally as associated with a business call from the caller afterselecting the button 308 to answer the call, report the phone number orlog locally as not associated with a business call from the caller afterselecting the button 308 to answer the call, and the like.

FIG. 3B illustrates an exemplary GUI 351 of the illustrative application(e.g., the application 194) for displaying caller ID information to theuser upon a call incoming at the mobile device. The GUI 351 may includea caller ID 352, and a set of buttons 303, 305, and 308 for the user toselect. Here, similar to the GUI 301, the caller ID 352 may beconfigured to display to the user that the call is from a callerassociated with a calling phone number, i.e., “1-347-000-0000,” and “NewYork” as the area associated with the phone number's area code. Thedifference here is that the calling phone number may have beendetermined as associated with a business call from the caller (e.g.,merchant), or, as described above with reference to FIG. 2 , the phonenumber may be determined as associated with a business call placedwithin the predicted caller's (e.g., merchant's) business hours (e.g.,11:55 may be determined as falling into the mechanic's business hours).Thus, the application 194 may be provided with the additionalinformation of the caller that pertains to the particular call. As aresult, at the GUI 351, the caller ID information may be augmented withthe caller's activity (e.g., activity type) information. For example,using the augmented information notified to the application 194, thedifferentiated caller ID information may be determined and displayed as“Mechanic's Call” even though the particular phone number“1-347-000-0000” is unknown to the user.

In some embodiments, and as shown here in FIG. 3B, the differentiatedcaller identification information determined may include furtherinformation with regard to the particular activity type (e.g., businessoperations) associated with the call differentiated from spam. In someimplementations, the information related to the particular activity typeengaged by the caller via initiating the particular call may be obtainedas described above with reference to FIG. 2 . By way of non-limitingexamples, once the call is differentiated as a business call from spam,the caller identification information may include information related tothe merchant, the merchant's business, and the like. In one example, theinformation related to the merchant's business may include thebusiness/service type of the merchant (e.g., eateries, grocery stores,mechanics, plumbers, catering, tax preparation service, dentists,physicians, baby-sitting, etc.). Here, as shown in FIG. 3B, in additionto determining the incoming call as associated with a Mechanic'sbusiness, the differentiated caller identification information may beaugmented to include the merchant's business address of “123 MainStreet.” As such, here in FIG. 3B, the GUI 351 may be configured todisplay the caller ID 352 as “Mechanic's Call 123 Main street from1-347-000-0000 New York.”

Here, at GUI 351, the user can also select the button 303 to screen theincoming call, select the button 305 to reply with a message, or selectthe button 308 to swipe up to answer the incoming call. The incomingcall can be screened by various techniques to evaluate thecharacteristics of the calling entity as described above.

Further, the user may also perform other actions upon the incoming callin addition to or in place of those illustrated in FIG. 3B. For example,the user can interact with the GUI 351 to decline the incoming callwhile it is pending, report the phone number included in the caller ID352 to the server (e.g., the server 101 of FIG. 1 ) or log it locally asassociated with a business call from the caller without screening thecall or after screening the call, report or log locally the phone numberas associated with a business call from the caller after selecting thebutton 308 to answer the call, report or log locally the phone number asnot associated with a business call from the caller after selecting thebutton 308 to answer the call, and the like.

FIG. 3C illustrates an exemplary GUI 381 for displaying caller IDinformation to the user upon a call incoming at the mobile device. TheGUI 381 may include a caller ID 382 and a set of buttons 303, 305, and308 for the user to select. Here, similar to the GUIs 301 and 351, thecaller ID 382 may be configured to display to the user that the call isfrom a caller associated with a calling phone number, i.e.,“1-347-000-0000,” and “New York” as the area associated with the phonenumber's area code. The difference here is that the calling phone numbermay have been determined as associated with a business call via thesecondary verification based on transaction data. In this example, theparticular call may be placed at a time that may be determined asoutside of the caller (e.g., merchant) business hours (e.g., 20:55 maybe determined as outside of a tax accountant's business hours althoughmay be as inside a restaurant's business hours). Nevertheless, theparticular call may be determined as associated with a business callbased on the caller's historical transactions (e.g., transactions withthe user, transactions with other similar users). In some embodiments, atransaction with the user may be incurred afterward (e.g., at the futurepoint of time after the particular call). For example, for a tax returnpreparation service provided by a CPA, the CPA may initiate the incomingcall with regard to fulfilling the service engaged with the user priorto the user completing a transaction with the CPA. In these scenarios,historical transactions may be used together with other suitablecontextual information pertinent to the call (e.g., how far into the taxseason, how close to the tax due date, called recently within theregular business hours) to perform the secondary verification. Here,even though the particular call is incoming from the CPA at a timeoutside of the CPA's published business hours, the particular call maybe determined as a business call based on the above describedtransactions and/or other information.

Similarly, the application may be provided with additional informationof the caller that pertains to the particular call. As a result, at theGUI 381, the caller ID information may be augmented with the determineddifferentiated caller identification information. As such, here in FIG.3C, the GUI 381 may be configured to display the caller ID 382 as “CPALee's Call from 1-347-000-0000 New York.”

Here, at GUI 381, the user can also select the button 303 to screen thecall, select the button 305 to reply with a message, or select thebutton 308 to swipe up to answer the incoming call. The incoming callcan be screened by various techniques to evaluate the characteristics ofthe calling entity as described above.

Further, the user may also perform other actions upon the incoming callin addition to or in place of those illustrated in FIG. 3C. For example,the user can interact with the GUI 381 to decline the incoming callwhile it is pending, report the phone number included in the caller ID382 to the server (e.g., the server 101 of FIG. 1 ) or log it locally asassociated with a business call from the caller without screening thecall or after screening the call, report or log locally the phone numberas associated with a business call from the caller after selecting thebutton 308 to answer the call, report or log locally the phone number asnot associated with a business call from the caller after selecting thebutton 308 to answer the call, and the like.

FIG. 4 is a flow diagram illustrating an exemplary process 400 relatedto caller identification differentiation via machine learningtechniques, consistent with exemplary aspects of at least someembodiments of the present disclosure. Referring to FIG. 4 , theillustrative caller identification differentiation process 400 maycomprise: obtaining a trained call differentiation machine learningmodel that determines a likelihood that a particular call associatedwith a particular phone number is of a first call type or a second calltype, where the first call type is associated with a first type ofactivity and the second call type is associated with a second type ofactivity, at 402; receiving a permission identifying a permission by thefirst user to detect calls being received by the computing device, at404; receiving an indication of a current call being received at acurrent time from a particular phone number that is associated with asecond user, at 406; utilizing the trained call differentiation machinelearning model to determine: an activity type associated with the seconduser, an activity qualifier associated with the second user based atleast in part on the activity type, and the likelihood that the currentcall is of the first call type or the second call type, based at leastin part on the current time and the activity qualifier, at 408; andinstructing, when the current call is of the first call type, acomputing device of the first user to present to the first user agraphical user interface (GUI) for displaying, to the first user,information related to the first type of activity of the second user, at410. In other embodiments, the caller identification differentiationprocess 400 may be carried out, in whole or in part, in conjunction witha server, a transacting device, and/or a mobile device that is connectedvia one or more networks to the server, which is executing instructionsfor performing one or more steps or aspects of various embodimentsdescribed herein.

In some embodiments, the caller identification differentiation process400 may include, at 402, a step of obtaining a trained calldifferentiation machine learning model that determines a likelihood thata particular call associated with a particular phone number is of afirst call type or a second call type, where the first call type isassociated with a first type of activity and the second call type isassociated with a second type of activity. With regard to the disclosedinnovation, the call differentiation machine learning model may betrained based at least in part on one or more of: (i) information of afirst plurality of users; (ii) activity information associated with afirst plurality of activities associated with the first plurality ofusers; (iii) phone number information of a first plurality of phonenumbers associated with a first plurality of calls from the firstplurality of users, the first plurality of calls including calls of atleast one of: the first call type or the second call type; (vi) timinginformation of when the first plurality of users engaging the firstplurality of activities and/or initiating the first plurality of calls;and/or (v) one or more of profile information, contextual information,transaction data involving the first plurality of users. In someimplementations, the first plurality of users may include one or moresmall business owners (e.g., merchants) who conduct activities that areat least differentiated as a first activity type and a second activitytype. Further, the first plurality of users may utilize the same phonenumbers to place phone calls related to activities of both the firstactivity type and the second activity type.

In some embodiments, the first plurality of training activityinformation associated with the first plurality of activities of thefirst plurality of users may include various information such as, butnot limited to, description of the activities, types of the activities,and so on. Exemplary activity types may include at least a firstactivity type (e.g., business related) and a second activity type(personal). In some embodiments, the first plurality of activities maybe associated with the first activity type. In some implementations, thefirst activity type may include a business operation type. For example,for a restaurant servicing lunches and dinners, the first plurality ofactivities of the restaurant owner may include operations related tofood preparation and servicing, compliance with regulations, as well asinteractions with customers, interaction with vendors, and the like. Insome embodiments, activities of the second activity type may includeinteractions with family members, friends, other service providers(e.g., merchants, dentists, physicians, etc.).

In some embodiments, the plurality of training phone calls may includevarious phone call events of the first call type, and/or the second calltype. In some embodiments, the first call type may correspond to thefirst activity type and include a business call type; while the secondcall type may correspond to the second activity type and include apersonal call type. In some embodiments, the training activityinformation and/or the training phone calls may include timinginformation pertaining to when the first plurality of users have engagedthe first plurality of activities, and initiated the first plurality ofcalls, respectively. Any suitable techniques may be applied to obtainsuch timing information for training. For example, for each call fromthe first plurality of users that is of the first call type, thetimestamps associated therewith may be used as the training timinginformation. For another example, information related to the timing whenthe first plurality of users has engaged in the first plurality ofactivities may be obtained or otherwise derived from various sources assuch, but not limited to, websites (e.g., Yelp, FourSquare, social mediasites, other website reporting the timing information), crowdsourcedtiming information compiled from communications from various customers,and so on. In some embodiments, the timing information related to thefirst plurality of activities may be obtained or derived from thetimestamps associated with the phone calls received and picked up (notgoing to voice mail) by the first plurality of users.

In some embodiments, additionally or separately, the calldifferentiation machine learning model may be trained with transactiondata associated with the first plurality of users. In some embodiments,the transaction data may include information related to historicaltransactions that the first plurality of users have processed inassociation with the first plurality of activities. For example, arestaurant owner may process one or more transactions authorized fromthe customers thereof from the night before, in the morning prior toopening up for lunch. In this scenario, the restaurant owner may placecall(s) to customers to confirm reservations, and the like. Here, basedon the above described training, the call differentiation machinelearning model may learn that the time the restaurant owner places suchcall(s) falls outside of the learned or known business hours of therestaurant. Nevertheless, given the patterns of the restaurant ownertends to catchup with the processing of transactions, the calldifferentiation machine learning model may predict that the restaurantowner is likely calling the customer(s) regarding his/her business.

In some embodiments, additionally or separately, the calldifferentiation machine learning model may be trained with suchtransaction data also associated with the call receiving parties of thefirst plurality of calls. In some embodiments, the transaction data mayinclude information related to historical transactions incurred by thefirst plurality of activities with these call receiving parties.Continuing from the example above, the restaurant owner may placecall(s) to one or more of his/her vendors during the morning time aswell. In this scenario, given the above described transaction patternsof the owner and the historical transactions with the vendor(s), thecall differentiation machine learning model may predict that, as thevendor(s) may probably be open for business during the morning, therestaurant owner is likely calling the vendor(s) regarding his/herbusiness. In another example, the restaurant owner may place call(s) tothe vendors during evening time when the restaurant is open forbusiness. Here, based on the above described training, the calldifferentiation machine learning model may learn that the time therestaurant owner places such call(s) falls within the learned or knownbusiness hours of the restaurant. Nevertheless, given the historicaltransactions with the vendor(s), the call differentiation machinelearning model may predict the call(s) as falling outside of thevendor's business hours and therefore, the restaurant owner may becalling for personal matters.

In some embodiments, the transaction data may include informationrelated to historical transactions incurred by the first plurality ofusers with the respective call receiving parties. In one example, therestaurant owner may place a call to his/her dentist to make anappointment during the morning prior to opening for business or duringbusiness hours. In this scenario, given the historical transactions withthe dentist, the call differentiation machine learning model may predictthat the restaurant owner is likely calling the dentist regardingpersonal matters, adjusting from the above-described prediction that therestaurant owner may be operating in a business mode outside of thebusiness hours (e.g., based on the pattern of processing transactions)or within the business hours. Various embodiments herein may beconfigured that the call differentiation machine learning model mayperform the illustrative activity type prediction based on the learnedbusiness hours and the illustrative secondary verification/adjustmentbased on transactions incurred by the first plurality of users and/ortransactions between the call receiving parties of the first pluralityof calls in any order, not limited by examples herein.

According to some aspects of the disclosure, equipped with the vastamount of data corresponding to calls of the first and/or secondactivity types, the exemplary trained call differentiation machinelearning model may predict first activity hours (e.g., business hours)for callers as an activity qualifier. Given the predicted first activityhours and the timestamp associated with a particular call incoming fromthe phone number associated with the caller, the call differentiationmodel may predict a likelihood of whether the particular call isassociated with an activity of the first activity type and therefore acall of the corresponding first call type (e.g., business call).

According to some aspects of the disclosure, the caller identificationdifferentiation machine learning model may be trained to use historicaltransactions (e.g., transactions processed by and/or transactionincurred by) associated with the caller of the particular call toperform secondary verification or adjustment with regard to theprediction based on the predicted business hours. As described above, insome embodiments, the call differentiation model may adjust theparticular call placed within the predicted business hours as personalcall based on, for example, historical transactions between the callerand the call receiving party to the particular call. In otherembodiments, the call differentiation model may adjust the particularcall placed outside the predicted business hours as business call basedon, for example, historical transactions processed by the caller, and/orhistorical transactions between the caller and the call receiving partyto the particular call.

The user profile information may comprise information relating to one ormore of: demographic information, account information, application usageinformation, any data provided by the user, any data provided on behalfof the user, type of business, location, employee information,management information, revenue information, press release information,product release information, stock information, privacy information, anydata provided by the user, and the like. The contextual aspect of theuser profile information and user contextual information may compriseinformation relating to one or more of: a timing, a location of theuser, an action of a user, calendar information of the user, contactinformation of the user, habits of the user, preferences of the user,purchase history of the user, browsing history of the user,communication history, travel history, on-line payment service history,contextual information related to the service provided by the user,profile and/or contextual information of individual(s) the user isassociated with, and the like. In some embodiments, the user profileinformation and/or user contextual information may be provided by theuser, detected by a server (e.g., the server 101 of FIG. 1 ), and/or acomponent external thereto, or in a combination thereof.

In some embodiments, the call differentiation machine learning model maybe trained via a server (e.g., the server 101 of FIG. 1 ), such as aprocessor of a computer platform, or an online computer platform. Insome embodiments, the processor is associated with an entity thatprovides a financial service to the user. Here, for example, the atleast one computer platform may comprise a financial service provider(FSP) system. This FSP system may comprise one or more servers and/orprocessors associated with a financial service entity that provides,maintains, manages, or otherwise offers financial services. Suchfinancial service entity may include a bank, credit card issuer, or anyother type of financial service entity that generates, provides,manages, and/or maintains financial service accounts for one or morecustomers. In other embodiments, the FSP system may outsource thetraining to a third-party model generator, or otherwise leverage thetraining data, and/or trained models from a third-party data source,third-party machine learning model generators, and the like.

It should be further understood that, in some embodiments, the calldifferentiation machine learning model may be trained/re-trained via aserver in conjunction with a computing device of the user. Here, forexample, the server may be configured to initially train a baseline calldifferentiation model based on the above-described training data of thefirst plurality of users and/or a plurality of such training data fromthe plurality of third-party data sources. Subsequently, the baselinecall differentiation model may be transmitted to the computing deviceassociated with the user (e.g., the call receiving users and/or callinitiating users) to be trained with the particular training data of theuser. In other words, a call differentiation model may be trained invarious manners and orders as a user-specific model in implementations.

In some embodiments, the call differentiation machine learning model maybe re-trained with various feedback data collected via the calleridentification differentiation process 400, and the like. For example,via the illustrative GUIs of FIGS. 3A-3C, a call receiving party mayreport or log locally whether or not a particular call from a callinitiating party is indeed a business call. In another example, theillustrative application 194 may monitor the content of the conservationconducted during the particular call and thereby automatically report orlog locally whether the particular call from the call initiating partyis indeed a business call. In yet another example, the illustrativeapplication 194 may screen the particular call (e.g., screen the voicemessage left by the particular call, etc.) and thereby automaticallyreport or log locally whether the particular call from the callinitiating party is indeed a business call.

The caller identification differentiation process 400 may include, at404, a step of receiving a permission indicator identifying a permissionby the first user to detect calls being received by the computingdevice; and at 406, a step of an indication of a current call beingreceived at a current time from a particular phone number that isassociated with a second user. In some embodiments, the permissionindicator may be received from an application such as the application194 executing on a computing device of the user. The details are similarto those described with reference to FIG. 1 , and not repeated herein.In other embodiments, the permission indicator may be received from anapplication such as a web page allowing the user to configure his or hersettings at a web browser. The user may configure the settings relatedto calls, and/or other communication detection capabilities for variouscomputing devices thereof. That is, the permission indicator may bereceived from an application and/or a computing device other than theapplication for detecting calls (and other communications), or thecomputing on which the call detecting/protection application isexecuting. In some embodiments, the indication of the current call maybe received from the above described computing device of the first user.In one embodiment, the indication of the current call may be receivedfrom the illustrative application 194 executing on the computing device.

The caller identification differentiation process 400 may include, at408, a step of utilizing the trained call differentiation machinelearning model to determine: an activity type associated with the seconduser, an activity qualifier associated with the second user based atleast in part on the activity type, and the likelihood that the currentcall is of the first call type or the second call type based at least inpart on the current time and the activity qualifier. In someembodiments, the activity type associated with the second user mayinclude a first type of activity and a second type of activity. In someimplementations, and as described above, the first type of activity mayinclude a business related type, and the second type of activity mayinclude a personal type. According to some aspects of the disclosure,historical transactions of the first user, of the second user, and/orbetween the first user and the second user may be utilized to determinethe activity type. For example, if the first user has incurred previoustransaction(s) with the second user, the first user might be a customerof the second user and hence the activity type may be a business relatedtype. In other embodiments, the activity type may be determined based onthe timing information (e.g., time of the day), and/or pertinentprofile/contextual information of the first user and/or the second user.In some embodiments, the activity qualifier may include information suchas, but not limited to, operating hours associated with the first typeof activity of the second user.

The caller identification differentiation process 400 may include, at410, a step of instructing, when the current call is of the first calltype, a computing device of the first user to present to the first usera graphical user interface (GUI) for displaying, to the first user,information related to the first type of activity of the second user. Insome embodiments, the computing device may include the computing deviceof the first user described above. As illustrated with reference toFIGS. 3B-3C, in some embodiments, the GUI may be presented inassociation with the current call. In some implementations, the GUI mayinclude at least one GUI element for displaying to the first user,information related to the first type of activity of the second user.Various implementations may be configured such that the informationrelated to the first type of activity of the second user may include atitle related to the first type of activity, an address related to thefirst type of activity, or a description of the first type of activitiesof the first user.

In some embodiments, the caller identification differentiationprotection process 400 may further include a step of utilizing the calldifferentiation machine learning model to determine an activity typeassociated with the first user; and determine whether the current callis of the first call type or the second call type based at least in parton the activity type associated with the first user. Various techniquesmay be applied to determine the activity type associated with the firstuser. In one example, similar to described above with reference to step408, the activity type associated with the first user may be predictedbased on historical transactions incurred by the second user with thefirst user. In this scenarios, based on other pertinent information withregard to the first user, the call differentiation model may predictwhether the first user's activity type is related to the activities ofthe first type of activity of the second user (e.g., vendor of thesecond user), or is related to services/goods provided by the first userhimself/herself (e.g., dentist of the second user). In some embodiments,the above described activity qualifier may be updated to include thedetermined activity type of the first user, and in turn utilized by thecall differentiation model to predict the call type for the particularcall.

FIG. 5 depicts a block diagram of an exemplary computer-basedsystem/platform in accordance with one or more embodiments of thepresent disclosure. However, not all of these components may be requiredto practice one or more embodiments, and variations in the arrangementand type of the components may be made without departing from the spiritor scope of various embodiments of the present disclosure. In someembodiments, the exemplary inventive computing devices and/or theexemplary inventive computing components of the exemplary computer-basedsystem/platform may be configured to manage a large number of instancesof software applications, users, and/or concurrent transactions, asdetailed herein. In some embodiments, the exemplary computer-basedsystem/platform may be based on a scalable computer and/or networkarchitecture that incorporates varies strategies for assessing the data,caching, searching, and/or database connection pooling. An example ofthe scalable architecture is an architecture that is capable ofoperating multiple servers.

In some embodiments, referring to FIG. 5 , members 702-704 (e.g.,clients) of the exemplary computer-based system/platform may includevirtually any computing device capable of receiving and sending amessage over a network (e.g., cloud network), such as network 705, toand from another computing device, such as servers 706 and 707, eachother, and the like. In some embodiments, the member devices 702-704 maybe configured to implement part of the entirety of the features andfunctionalities above-described for the computing device 180 of FIG. 1 .In some embodiments, the servers 706 and 707 may be configured toimplement part of the entirety of the features and functionalitiesabove-described for the server 101 of FIG. 1 . In some embodiments, themember devices 702-704 may be personal computers, multiprocessorsystems, microprocessor-based or programmable consumer electronics,network PCs, and the like. In some embodiments, one or more memberdevices within member devices 702-704 may include computing devices thattypically connect using wireless communications media such as cellphones, smart phones, pagers, walkie talkies, radio frequency (RF)devices, infrared (IR) devices, CBs, integrated devices combining one ormore of the preceding devices, or virtually any mobile computing device,and the like. In some embodiments, one or more member devices withinmember devices 702-704 may be devices that are capable of connectingusing a wired or wireless communication medium such as a PDA, POCKET PC,wearable computer, a laptop, tablet, desktop computer, a netbook, avideo game device, a pager, a smart phone, an ultra-mobile personalcomputer (UMPC), and/or any other device that is equipped to communicateover a wired and/or wireless communication medium (e.g., NFC, RFID,NBIOT, 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, ZigBee,etc.). In some embodiments, one or more member devices within memberdevices 702-704 may include one or more applications, such as Internetbrowsers, mobile applications, voice calls, video games,videoconferencing, and email, among others. In some embodiments, one ormore member devices within member devices 702-704 may be configured toreceive and to send web pages, and the like. In some embodiments, anexemplary specifically programmed browser application of the presentdisclosure may be configured to receive and display graphics, text,multimedia, and the like, employing virtually any web based language,including, but not limited to Standard Generalized Markup Language(SMGL), such as HyperText Markup Language (HTML), a wireless applicationprotocol (WAP), a Handheld Device Markup Language (HDML), such asWireless Markup Language (WML), WMLScript, XML, JavaScript, and thelike. In some embodiments, a member device within member devices 702-704may be specifically programmed by either Java, .Net, QT, C, C++ and/orother suitable programming language. In some embodiments, one or moremember devices within member devices 702-704 may be specificallyprogrammed include or execute an application to perform a variety ofpossible tasks, such as, without limitation, messaging functionality,browsing, searching, playing, streaming or displaying various forms ofcontent, including locally stored or uploaded messages, images and/orvideo, and/or games.

In some embodiments, the exemplary network 705 may provide networkaccess, data transport and/or other services to any computing devicecoupled to it. In some embodiments, the exemplary network 705 mayinclude and implement at least one specialized network architecture thatmay be based at least in part on one or more standards set by, forexample, without limitation, GlobalSystem for Mobile communication (GSM)Association, the Internet Engineering Task Force (IETF), and theWorldwide Interoperability for Microwave Access (WiMAX) forum. In someembodiments, the exemplary network 705 may implement one or more of aGSM architecture, a General Packet Radio Service (GPRS) architecture, aUniversal Mobile Telecommunications System (UMTS) architecture, and anevolution of UMTS referred to as Long Term Evolution (LTE). In someembodiments, the exemplary network 705 may include and implement, as analternative or in conjunction with one or more of the above, a WiMAXarchitecture defined by the WiMAX forum. In some embodiments and,optionally, in combination of any embodiment described above or below,the exemplary network 705 may also include, for instance, at least oneof a local area network (LAN), a wide area network (WAN), the Internet,a virtual LAN (VLAN), an enterprise LAN, a layer 3 virtual privatenetwork (VPN), an enterprise IP network, or any combination thereof. Insome embodiments and, optionally, in combination of any embodimentdescribed above or below, at least one computer network communicationover the exemplary network 705 may be transmitted based at least in parton one of more communication modes such as but not limited to: NFC,RFID, Narrow Band Internet of Things (NBIOT), ZigBee, 3G, 4G, 5G, GSM,GPRS, WiFi, WiMax, CDMA, satellite and any combination thereof. In someembodiments, the exemplary network 705 may also include mass storage,such as network attached storage (NAS), a storage area network (SAN), acontent delivery network (CDN) or other forms of computer- ormachine-readable media.

In some embodiments, the exemplary server 706 or the exemplary server707 may be a web server (or a series of servers) running a networkoperating system, examples of which may include but are not limited toMicrosoft Windows Server, Novell NetWare, or Linux. In some embodiments,the exemplary server 706 or the exemplary server 707 may be used forand/or provide cloud and/or network computing. Although not shown inFIG. 5 , in some embodiments, the exemplary server 706 or the exemplaryserver 707 may have connections to external systems like email, SMSmessaging, text messaging, ad content sources, etc. Any of the featuresof the exemplary server 706 may also be implemented in the exemplaryserver 707 and vice versa.

In some embodiments, one or more of the exemplary servers 706 and 707may be specifically programmed to perform, in non-limiting example, asauthentication servers, search servers, email servers, social networkingservices servers, SMS servers, IM servers, MMS servers, exchangeservers, photo-sharing services servers, advertisement providingservers, financial/banking-related services servers, travel servicesservers, or any similarly suitable service-base servers for users of themember computing devices 701-704.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, for example, one or more exemplary computingmember devices 702-704, the exemplary server 706, and/or the exemplaryserver 707 may include a specifically programmed software module thatmay be configured to send, process, and receive information using ascripting language, a remote procedure call, an email, a tweet, ShortMessage Service (SMS), Multimedia Message Service (MMS), instantmessaging (IM), internet relay chat (IRC), mIRC, Jabber, an applicationprogramming interface, Simple Object Access Protocol (SOAP) methods,Common Object Request Broker Architecture (CORBA), HTTP (HypertextTransfer Protocol), REST (Representational State Transfer), or anycombination thereof.

FIG. 6 depicts a block diagram of another exemplary computer-basedsystem/platform 800 in accordance with one or more embodiments of thepresent disclosure. However, not all of these components may be requiredto practice one or more embodiments, and variations in the arrangementand type of the components may be made without departing from the spiritor scope of various embodiments of the present disclosure. In someembodiments, the member computing devices (e.g., clients) 802 a, 802 bthrough 802 n shown each at least includes non-transitorycomputer-readable media, such as a random-access memory (RAM) 808coupled to a processor 810 and/or memory 808. In some embodiments, themember computing devices 802 a, 802 b through 802 n may be configured toimplement part of the entirety of the features and functionalitiesabove-described for the computing device 180 of FIG. 1 . In someembodiments, the processor 810 may execute computer-executable programinstructions stored in memory 808. In some embodiments, the processor810 may include a microprocessor, an ASIC, and/or a state machine. Insome embodiments, the processor 810 may include, or may be incommunication with, media, for example computer-readable media, whichstores instructions that, when executed by the processor 810, may causethe processor 810 to perform one or more steps described herein. In someembodiments, examples of computer-readable media may include, but arenot limited to, an electronic, optical, magnetic, or other storage ortransmission device capable of providing a processor, such as theprocessor 810 of client 802 a, with computer-readable instructions. Insome embodiments, other examples of suitable non-transitory media mayinclude, but are not limited to, a floppy disk, CD-ROM, DVD, magneticdisk, memory chip, ROM, RAM, an ASIC, a configured processor, alloptical media, all magnetic tape or other magnetic media, or any othermedia from which a computer processor can read instructions. Also,various other forms of computer-readable media may transmit or carryinstructions to a computer, including a router, private or publicnetwork, or other transmission device or channel, both wired andwireless. In some embodiments, the instructions may comprise code fromany computer-programming language, including, for example, C, C++,Visual Basic, Java, Python, Perl, JavaScript, and etc.

In some embodiments, member computing devices 802 a through 802 n mayalso comprise a number of external or internal devices such as a mouse,a CD-ROM, DVD, a physical or virtual keyboard, a display, a speaker, orother input or output devices. In some embodiments, examples of membercomputing devices 802 a through 802 n (e.g., clients) may be any type ofprocessor-based platforms that are connected to a network 806 such as,without limitation, personal computers, digital assistants, personaldigital assistants, smart phones, pagers, digital tablets, laptopcomputers, Internet appliances, and other processor-based devices. Insome embodiments, member computing devices 802 a through 802 n may bespecifically programmed with one or more application programs inaccordance with one or more principles/methodologies detailed herein. Insome embodiments, member computing devices 802 a through 802 n mayoperate on any operating system capable of supporting a browser orbrowser-enabled application, such as Microsoft™, Windows™, and/or Linux.In some embodiments, member computing devices 802 a through 802 n shownmay include, for example, personal computers executing a browserapplication program such as Microsoft Corporation's Internet Explorer™,Apple Computer, Inc.'s Safari™, Mozilla Firefox, and/or Opera. In someembodiments, through the member computing client devices 802 a through802 n, users, 812 a through 812 n, may communicate over the exemplarynetwork 806 with each other and/or with other systems and/or devicescoupled to the network 806.

As shown in FIG. 6 , exemplary server devices 804 and 813 may be alsocoupled to the network 806. In some embodiments, one or more membercomputing devices 802 a through 802 n may be mobile clients. In someembodiments, the server devices 804 and 813 may be configured toimplement part of the entirety of the features and functionalitiesabove-described for the server 101 of FIG. 1 . In some embodiments,server devices 804 and 813 shown each at least includes respectivecomputer-readable media, such as a random-access memory (RAM) coupled toa respective processor 805, 814 and/or respective memory 817, 816. Insome embodiments, the processor 805, 814 may execute computer-executableprogram instructions stored in memory 817, 816, respectively. In someembodiments, the processor 805, 814 may include a microprocessor, anASIC, and/or a state machine. In some embodiments, the processor 805,814 may include, or may be in communication with, media, for examplecomputer-readable media, which stores instructions that, when executedby the processor 805, 814, may cause the processor 805, 814 to performone or more steps described herein. In some embodiments, examples ofcomputer-readable media may include, but are not limited to, anelectronic, optical, magnetic, or other storage or transmission devicecapable of providing a processor, such as the respective processor 805,814 of server devices 804 and 813, with computer-readable instructions.In some embodiments, other examples of suitable media may include, butare not limited to, a floppy disk, CD-ROM, DVD, magnetic disk, memorychip, ROM, RAM, an ASIC, a configured processor, all optical media, allmagnetic tape or other magnetic media, or any other media from which acomputer processor can read instructions. Also, various other forms ofcomputer-readable media may transmit or carry instructions to acomputer, including a router, private or public network, or othertransmission device or channel, both wired and wireless. In someembodiments, the instructions may comprise code from anycomputer-programming language, including, for example, C, C++, VisualBasic, Java, Python, Perl, JavaScript, and etc.

In some embodiments, at least one database of exemplary databases 807and 815 may be any type of database, including a database managed by adatabase management system (DBMS). In some embodiments, an exemplaryDBMS-managed database may be specifically programmed as an engine thatcontrols organization, storage, management, and/or retrieval of data inthe respective database. In some embodiments, the exemplary DBMS-manageddatabase may be specifically programmed to provide the ability to query,backup and replicate, enforce rules, provide security, compute, performchange and access logging, and/or automate optimization. In someembodiments, the exemplary DBMS-managed database may be chosen fromOracle database, IBM DB2, Adaptive Server Enterprise, FileMaker,Microsoft Access, Microsoft SQL Server, MySQL, PostgreSQL, and a NoSQLimplementation. In some embodiments, the exemplary DBMS-managed databasemay be specifically programmed to define each respective schema of eachdatabase in the exemplary DBMS, according to a particular database modelof the present disclosure which may include a hierarchical model,network model, relational model, object model, or some other suitableorganization that may result in one or more applicable data structuresthat may include fields, records, files, and/or objects. In someembodiments, the exemplary DBMS-managed database may be specificallyprogrammed to include metadata about the data that is stored.

As also shown in FIGS. 7 and 8 , some embodiments of the disclosedtechnology may also include and/or involve one or more cloud components825, which are shown grouped together in the drawing for sake ofillustration, though may be distributed in various ways. Cloudcomponents 825 may include one or more cloud services such as softwareapplications (e.g., queue, etc.), one or more cloud platforms (e.g., aWeb front-end, etc.), cloud infrastructure (e.g., virtual machines,etc.), and/or cloud storage (e.g., cloud databases, etc.).

According to some embodiments shown by way of one example in FIG. 8 ,the exemplary inventive computer-based systems/platforms, the exemplaryinventive computer-based devices, components and media, and/or theexemplary inventive computer-implemented methods of the presentdisclosure may be specifically configured to operate in or with cloudcomputing/architecture such as, but not limiting to: infrastructure aservice (IaaS) 1010, platform as a service (PaaS) 1008, and/or softwareas a service (SaaS) 1006. FIGS. 7 and 8 illustrate schematics ofexemplary implementations of the cloud computing/architecture(s) inwhich the exemplary inventive computer-based systems/platforms, theexemplary inventive computer-implemented methods, and/or the exemplaryinventive computer-based devices, components and/or media of the presentdisclosure may be specifically configured to operate. In someembodiments, such cloud architecture 1006, 1008, 1010 may be utilized inconnection with the Web browser and browser extension aspects, shown at1004, to achieve the innovations herein.

As used in the description and in any claims, the term “based on” is notexclusive and allows for being based on additional factors notdescribed, unless the context clearly dictates otherwise. In addition,throughout the specification, the meaning of “a,” “an,” and “the”include plural references. The meaning of “in” includes “in” and “on.”

It is understood that at least one aspect/functionality of variousembodiments described herein can be performed in real-time and/ordynamically. As used herein, the term “real-time” is directed to anevent/action that can occur instantaneously or almost instantaneously intime when another event/action has occurred. For example, the “real-timeprocessing,” “real-time computation,” and “real-time execution” allpertain to the performance of a computation during the actual time thatthe related physical process (e.g., a user interacting with anapplication on a mobile device) occurs, in order that results of thecomputation can be used in guiding the physical process.

As used herein, the term “dynamically” and term “automatically,” andtheir logical and/or linguistic relatives and/or derivatives, mean thatcertain events and/or actions can be triggered and/or occur without anyhuman intervention. In some embodiments, events and/or actions inaccordance with the present disclosure can be in real-time and/or basedon a predetermined periodicity of at least one of: nanosecond, severalnanoseconds, millisecond, several milliseconds, second, several seconds,minute, several minutes, hourly, several hours, daily, several days,weekly, monthly, etc.

As used herein, the term “runtime” corresponds to any behavior that isdynamically determined during an execution of a software application orat least a portion of software application.

In some embodiments, exemplary inventive, specially programmed computingsystems/platforms with associated devices (e.g., the server 101, and/orthe computing device 180 illustrated in FIG. 1 ) are configured tooperate in the distributed network environment, communicating with oneanother over one or more suitable data communication networks (e.g., theInternet, satellite, etc.) and utilizing one or more suitable datacommunication protocols/modes such as, without limitation, IPX/SPX,X.25, AX.25, AppleTalk(™), TCP/IP (e.g., HTTP), Bluetooth™, near-fieldwireless communication (NFC), RFID, Narrow Band Internet of Things(NBIOT), 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, ZigBee,and other suitable communication modes. Various embodiments herein mayinclude interactive posters that involve wireless, e.g., Bluetooth™and/or NFC, communication aspects, as set forth in more detail furtherbelow. In some embodiments, the NFC can represent a short-range wirelesscommunications technology in which NFC-enabled devices are “swiped,”“bumped,” “tap” or otherwise moved in close proximity to communicate. Insome embodiments, the NFC could include a set of short-range wirelesstechnologies, typically requiring a distance of 10 cm or less. In someembodiments, the NFC may operate at 13.56 MHz on ISO/IEC 18000-3 airinterface and at rates ranging from 106 kbit/s to 424 kbit/s. In someembodiments, the NFC can involve an initiator and a target; theinitiator actively generates an RF field that can power a passivetarget. In some embodiments, this can enable NFC targets to take verysimple form factors such as tags, stickers, key fobs, or cards that donot require batteries. In some embodiments, the NFC's peer-to-peercommunication can be conducted when a plurality of NFC-enable devices(e.g., smartphones) are within close proximity of each other.

The material disclosed herein may be implemented in software or firmwareor a combination of them or as instructions stored on a machine-readablemedium, which may be read and executed by one or more processors. Amachine-readable medium may include any medium and/or mechanism forstoring or transmitting information in a form readable by a machine(e.g., a computing device). For example, a machine-readable medium mayinclude read only memory (ROM); random access memory (RAM); magneticdisk storage media; optical storage media; flash memory devices;electrical, optical, acoustical or other forms of propagated signals(e.g., carrier waves, infrared signals, digital signals, etc.), andothers.

As used herein, the terms “computer engine” and “engine” identify atleast one software component and/or a combination of at least onesoftware component and at least one hardware component which aredesigned/programmed/configured to manage/control other software and/orhardware components (such as the libraries, software development kits(SDKs), objects, etc.).

Examples of hardware elements may include processors, microprocessors,circuits, circuit elements (e.g., transistors, resistors, capacitors,inductors, and so forth), integrated circuits, application specificintegrated circuits (ASIC), programmable logic devices (PLD), digitalsignal processors (DSP), field programmable gate array (FPGA), logicgates, registers, semiconductor device, chips, microchips, chip sets,and so forth. In some embodiments, the one or more processors may beimplemented as a Complex Instruction Set Computer (CISC) or ReducedInstruction Set Computer (RISC) processors; x86 instruction setcompatible processors, multi-core, or any other microprocessor orcentral processing unit (CPU). In various implementations, the one ormore processors may be dual-core processor(s), dual-core mobileprocessor(s), and so forth.

Examples of software may include software components, programs,applications, computer programs, application programs, system programs,machine programs, operating system software, middleware, firmware,software modules, routines, subroutines, functions, methods, procedures,software interfaces, application program interfaces (API), instructionsets, computing code, computer code, code segments, computer codesegments, words, values, symbols, or any combination thereof.Determining whether an embodiment is implemented using hardware elementsand/or software elements may vary in accordance with any number offactors, such as desired computational rate, power levels, heattolerances, processing cycle budget, input data rates, output datarates, memory resources, data bus speeds and other design or performanceconstraints.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, identified as “IP cores,” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that make the logic or processor. Of note, various embodimentsdescribed herein may, of course, be implemented using any appropriatehardware and/or computing software languages (e.g., C++, Objective-C,Swift, Java, JavaScript, Python, Perl, QT, etc.).

In some embodiments, one or more of exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay include or be incorporated, partially or entirely into at least onepersonal computer (PC), laptop computer, ultra-laptop computer, tablet,touch pad, portable computer, handheld computer, palmtop computer,personal digital assistant (PDA), cellular telephone, combinationcellular telephone/PDA, television, smart device (e.g., smart phone,smart tablet or smart television), mobile internet device (MID),messaging device, data communication device, and so forth.

As used herein, the term “server” should be understood to refer to aservice point which provides processing, database, and communicationfacilities. By way of example, and not limitation, the term “server” canrefer to a single, physical processor with associated communications anddata storage and database facilities, or it can refer to a networked orclustered complex of processors and associated network and storagedevices, as well as operating software and one or more database systemsand application software that support the services provided by theserver. Cloud components (e.g., FIG. 7-8 ) and cloud servers areexamples.

In some embodiments, as detailed herein, one or more of exemplaryinventive computer-based systems/platforms, exemplary inventivecomputer-based devices, and/or exemplary inventive computer-basedcomponents of the present disclosure may obtain, manipulate, transfer,store, transform, generate, and/or output any digital object and/or dataunit (e.g., from inside and/or outside of a particular application) thatcan be in any suitable form such as, without limitation, a file, acontact, a task, an email, a social media post, a map, an entireapplication (e.g., a calculator), etc. In some embodiments, as detailedherein, one or more of exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be implemented across one or more of various computer platforms suchas, but not limited to: (1) FreeBSD™, NetBSD™, OpenBSD™; (2) Linux™; (3)Microsoft Windows™; (4) OS X (MacOS)™; (5) MacOS 11™; (6) Solaris™; (7)Android™; (8) iOS™; (9) Embedded Linux™; (10) Tizen™; (11) WebOS™; (12)IBM i™; (13) IBM AIX™; (14) Binary Runtime Environment for Wireless(BREW)™; (15) Cocoa (API)™; (16) Cocoa Touch™; (17) Java Platforms™;(18) JavaFX™; (19) JavaFX Mobile;™(20) Microsoft DirectX™; (21) .NETFramework™; (22) Silverlight™; (23) Open Web Platform™; (24) OracleDatabase™; (25) Qt™; (26) Eclipse Rich Client Platform™; (27) SAPNetWeaver™; (28) Smartface™; and/or (29) Windows Runtime™.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to utilize hardwired circuitry that may be used inplace of or in combination with software instructions to implementfeatures consistent with principles of the disclosure. Thus,implementations consistent with principles of the disclosure are notlimited to any specific combination of hardware circuitry and software.For example, various embodiments may be embodied in many different waysas a software component such as, without limitation, a stand-alonesoftware package, a combination of software packages, or it may be asoftware package incorporated as a “tool” in a larger software product.

For example, exemplary software specifically programmed in accordancewith one or more principles of the present disclosure may bedownloadable from a network, for example, a website, as a stand-aloneproduct or as an add-in package for installation in an existing softwareapplication. For example, exemplary software specifically programmed inaccordance with one or more principles of the present disclosure mayalso be available as a client-server software application, or as aweb-enabled software application. For example, exemplary softwarespecifically programmed in accordance with one or more principles of thepresent disclosure may also be embodied as a software package installedon a hardware device.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to output to distinct, specifically programmedgraphical user interface implementations of the present disclosure(e.g., a desktop, a web app., etc.). In various implementations of thepresent disclosure, a final output may be displayed on a displayingscreen which may be, without limitation, a screen of a computer, ascreen of a mobile device, or the like. In various implementations, thedisplay may be a holographic display. In various implementations, thedisplay may be a transparent surface that may receive a visualprojection. Such projections may convey various forms of information,images, and/or objects. For example, such projections may be a visualoverlay for a mobile augmented reality (MAR) application.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to be utilized in various applications which mayinclude, but not limited to, gaming, mobile-device games, video chats,video conferences, live video streaming, video streaming and/oraugmented reality applications, mobile-device messenger applications,and others similarly suitable computer-device applications.

As used herein, the term “mobile electronic device,” or the like, mayrefer to any portable electronic device that may or may not be enabledwith location tracking functionality (e.g., MAC address, InternetProtocol (IP) address, or the like). For example, a mobile electronicdevice can include, but is not limited to, a mobile phone, PersonalDigital Assistant (PDA), Blackberry™, Pager, Smartphone, smart watch, orany other reasonable mobile electronic device.

As used herein, the terms “cloud,” “Internet cloud,” “cloud computing,”“cloud architecture,” and similar terms correspond to at least one ofthe following: (1) a large number of computers connected through areal-time communication network (e.g., Internet); (2) providing theability to run a program or application on many connected computers(e.g., physical machines, virtual machines (VMs)) at the same time; (3)network-based services, which appear to be provided by real serverhardware, and are in fact served up by virtual hardware (e.g., virtualservers), simulated by software running on one or more real machines(e.g., allowing to be moved around and scaled up (or down) on the flywithout affecting the end user).

The aforementioned examples are, of course, illustrative and notrestrictive.

As used herein, the term “user” shall have a meaning of at least oneuser. In some embodiments, the terms “user”, “subscriber”, “consumer”,or “customer” should be understood to refer to a user of an applicationor applications as described herein and/or a consumer of data suppliedby a data provider/source. By way of example, and not limitation, theterms “user” or “subscriber” can refer to a person who receives dataprovided by the data or service provider over the Internet in a browsersession, or can refer to an automated software application whichreceives the data and stores or processes the data.

At least some aspects of the present disclosure will now be describedwith reference to the following numbered clauses.

Clause 1. A method including:

-   -   obtaining, by one or more processors, a trained call        differentiation machine learning model that determines a        likelihood that a particular call associated with a particular        phone number is of a first call type or a second call type,        wherein the first call type is associated with a first type of        activity and the second call type is associated with a second        type of activity;    -   receiving, by the one or more processors, from a computing        device of a first user, a permission indicator identifying a        permission by the first user to detect calls being received by        the computing device;    -   receiving, by the one or more processors, from the computing        device, an indication of a current call being received at a        current time from a particular phone number that is associated        with a second user;    -   utilizing, by the one or more processors, the trained call        differentiation machine learning model to:    -   determine an activity type associated with the second user,    -   determining an activity qualifier associated with the second        user based at least in part on the activity type, and    -   determine the likelihood that the current call is of the first        call type or the second call type, based at least in part on the        current time and the activity qualifier; and    -   instructing, by the one or more processors, when the current        call is of the first call type, the computing device of the        first user to present to the first user, a graphical user        interface (GUI) associated with the current call, wherein the        GUI comprises at least one GUI element, displaying, to the first        user, information related to the first type of activity of the        second user.

Clause 2. The method of clause 1 or any clause herein, where the trainedcall differentiating

-   -   machine learning model has been trained based on:    -   information of a first plurality of users,    -   activity information associated with a first plurality of        activities associated with the first plurality of users,    -   phone number information of a first plurality of phone numbers        associated with a first plurality of calls from the first        plurality of users, the first plurality of calls including calls        of at least one of: the first call type or the second call type,    -   timing information of when the first plurality of users engaging        the first plurality of activities and/or initiating the first        plurality of calls, and    -   at least one of:    -   a) profile information of the first plurality of users; or    -   b) contextual information associated with the first plurality of        users.

Clause 3. The method of clause 1 or any clause herein, where theinformation related to the first type of activity of the second usercomprises at least one of: a title related to the first type ofactivity, an address related to the first type of activity, or adescription of the first type of activities of the second user.

Clause 4. The method of clause 1 or any clause herein, where the calldifferentiation machine learning model is retrained based on feedbackdata from the second user, the feedback data including at least anindication whether the call is of the first call type.

Clause 5. The method of clause 1 or any clause herein, where the call ismonitored to obtain feedback data with regard to whether the call is ofthe first call type.

Clause 6. The method of clause 1 or any clause herein, furthercomprising:

-   -   utilizing, by the one or more processors, the call        differentiation machine learning model to determine an activity        type associated with the first user; and    -   determining, by the one or more processors, the current call is        of the second call type or the second call type, based at least        in part on the activity type associated with the first user.

Clause 7. The method of clause 6 or any clause herein, where theactivity type associated with the first user is determined based atleast in part on a transaction incurred by the second user with thefirst user.

Clause 8. The method of clause 1 or any clause herein, where theactivity type associated with the second user is determined based on atleast a transaction incurred by the first user with the second user.Clause 9. A system including:

-   -   one or more processors; and    -   a memory in communication with the one or more processors and        storing instructions that, when executed by the one or more        processors, cause the one or more processors to:        -   obtain a trained call differentiation machine learning model            that determines a likelihood that a particular call            associated with a particular phone number is of a first call            type or a second call type, wherein the first call type is            associated with a first type of activity and the second call            type is associated with a second type of activity;        -   receive from a computing device of a first user, a            permission indicator identifying a permission by the first            user to detect calls being received by the computing device;    -   receive from the computing device, an indication of a current        call being received at a current time from a particular phone        number that is associated with a second user;    -   utilize the trained call differentiation machine learning model        to:        -   determine an activity type associated with the second user,        -   determining an activity qualifier associated with the second            user based at least in part on the activity type, and        -   determine the likelihood that the current call is of the            first call type or the second call type, based at least in            part on the current time and the activity qualifier; and    -   instruct, when the current call is of the first call type, the        computing device of the first user to present to the first user,        a graphical user interface (GUI) associated with the current        call, wherein the GUI comprises at least one GUI element,        displaying, to the first user, information related to the first        type of activity of the second user.

Clause 10. The system of clause 9 or any clause herein, where whereinthe trained call differentiating machine learning model has been trainedbased on:

-   -   information of a first plurality of users,    -   activity information associated with a first plurality of        activities associated with the first plurality of users,    -   phone number information of a first plurality of phone numbers        associated with a first plurality of calls from the first        plurality of users, the first plurality of calls including calls        of at least one of: the first call type or the second call type,    -   timing information of when the first plurality of users engaging        the first plurality of activities and/or initiating the first        plurality of calls, and    -   at least one of:    -   a) profile information of the first plurality of users; or    -   b) contextual information associated with the first plurality of        users.

Clause 11. The system of clause 9 or any clause herein, where theinformation related to the first type of activity of the second usercomprises at least one of: a title related to the first type ofactivity, an address related to the first type of activity, or adescription of the first type of activities of the second user.

Clause 12. The system of clause 9 or any clause herein, where the calldifferentiation machine learning model is retrained based on feedbackdata from the second user, the feedback data including at least anindication whether the call is of the first call type.

Clause 13. The system of clause 9 or any clause herein, where the callis monitored to obtain feedback data with regard to whether the call isof the first call type.

Clause 14. The system of clause 9 or any clause herein, where the one ormore processors are further caused to:

-   -   utilize the call differentiation machine learning model to        determine an activity type associated with the first user; and    -   determine the current call is of the second call type or the        second call type, based at least in part on the activity type        associated with the first user.

Clause 15. A non-transitory computer readable storage medium fortangibly storing computer program instructions capable of being executedby a computer processor, the computer program instructions defining thesteps of:

-   -   obtaining a trained call differentiation machine learning model        that determines a likelihood that a particular call associated        with a particular phone number is of a first call type or a        second call type, wherein the first call type is associated with        a first type of activity and the second call type is associated        with a second type of activity;    -   receiving, from a computing device of a first user, a permission        indicator identifying a permission by the first user to detect        calls being received by the computing device;    -   receiving, from the computing device, an indication of a current        call being received at a current time from a particular phone        number that is associated with a second user;    -   utilizing the trained call differentiation machine learning        model to:    -   determine an activity type associated with the second user,    -   determining an activity qualifier associated with the second        user based at least in part on the activity type, and    -   determine the likelihood that the current call is of the first        call type or the second call type, based at least in part on the        current time and the activity qualifier; and    -   instructing, when the current call is of the first call type,        the computing device of the first user to present to the first        user, a graphical user interface (GUI) associated with the        current call, wherein the GUI comprises at least one GUI        element, displaying, to the first user, information related to        the first type of activity of the second user.

Clause 16. The computer readable storage medium of clause 15 or anyclause herein, the trained call differentiating machine learning modelhas been trained based on: information of a first plurality of users,

-   -   activity information associated with a first plurality of        activities associated with the first plurality of users,    -   phone number information of a first plurality of phone numbers        associated with a first plurality of calls from the first        plurality of users, the first plurality of calls including calls        of at least one of: the first call type or the second call type,    -   timing information of when the first plurality of users engaging        the first plurality of activities and/or initiating the first        plurality of calls, and    -   at least one of:    -   a) profile information of the first plurality of users; or    -   b) contextual information associated with the first plurality of        users

Clause 17. The computer readable storage medium of clause 15 or anyclause herein, where the information related to the first type ofactivity of the second user comprises at least one of: a title relatedto the first type of activity, an address related to the first type ofactivity, or a description of the first type of activities of the seconduser.

Clause 18. The computer readable storage medium of clause 15 or anyclause herein, where the call differentiation machine learning model isretrained based on feedback data from the second user, the feedback dataincluding at least an indication whether the call is of the first calltype.

Clause 19. The computer readable storage medium of clause 15 or anyclause herein, the steps further comprising:

-   -   utilizing the call differentiation machine learning model to        determine an activity type associated with the first user; and    -   determining the current call is of the second call type or the        second call type, based at least in part on the activity type        associated with the first user.

Clause 20. The computer readable storage medium of clause 19 or anyclause herein, where the activity type associated with the first user isdetermined based at least in part on a transaction incurred by thesecond user with the first user.

While one or more embodiments of the present disclosure have beendescribed, it is understood that these embodiments are illustrativeonly, and not restrictive, and that many modifications may becomeapparent to those of ordinary skill in the art, including that variousembodiments of the inventive methodologies, the inventivesystems/platforms, and the inventive devices described herein can beutilized in any combination with each other. Further still, the varioussteps may be carried out in any desired order (and any desired steps maybe added and/or any desired steps may be eliminated).

What is claimed is:
 1. A method comprising: receiving, by one or moreprocessors, from a computing device of a first user, an indication of acurrent call being received at a current time from a particular phonenumber that is associated with a second user; utilizing, by the one ormore processors, a trained call differentiation machine learning modelthat determines a likelihood that a particular call associated with aparticular phone number is of a first call type or a second call type,wherein the first call type is associated with a first type of activityand the second call type is associated with a second type of activity,wherein the trained call differentiation machine learning model isconfigured to: determine an activity type associated with the seconduser, determining an activity qualifier associated with the second userbased at least in part on the activity type, and determine thelikelihood that the current call is of the first call type or the secondcall type, based at least in part on the current time and the activityqualifier; and instructing, by the one or more processors, when thecurrent call is of the first call type, the computing device of thefirst user to present to the first user, a graphical user interface(GUI) associated with the current call, wherein the GUI comprises atleast one GUI element, displaying, to the first user, informationrelated to the first type of activity of the second user.
 2. The methodof claim 1, wherein the trained call differentiating machine learningmodel has been trained based on: information of a first plurality ofusers, activity information associated with a first plurality ofactivities associated with the first plurality of users, phone numberinformation of a first plurality of phone numbers associated with afirst plurality of calls from the first plurality of users, the firstplurality of calls including calls of at least one of: the first calltype or the second call type, timing information of when the firstplurality of users engaging the first plurality of activities and/orinitiating the first plurality of calls, and at least one of: a) profileinformation of the first plurality of users; or b) contextualinformation associated with the first plurality of users.
 3. The methodof claim 1, wherein the information related to the first type ofactivity of the second user comprises at least one of: a title relatedto the first type of activity, an address related to the first type ofactivity, or a description of the first type of activities of the seconduser.
 4. The method of claim 1, wherein the call differentiation machinelearning model is retrained based on feedback data from the second user,the feedback data including at least an indication whether the call isof the first call type.
 5. The method of claim 4, wherein the call ismonitored to obtain feedback data with regard to whether the call is ofthe first call type.
 6. The method of claim 1, further comprising:utilizing, by the one or more processors, the call differentiationmachine learning model to determine an activity type associated with thefirst user; and determining, by the one or more processors, the currentcall is of the first call type or the second call type, based at leastin part on the activity type associated with the first user.
 7. Themethod of claim 6, wherein the activity type associated with the firstuser is determined based at least in part on a transaction incurred bythe second user with the first user.
 8. The method of claim 1, whereinthe activity type associated with the second user is determined based onat least a transaction incurred by the first user with the second user.9. A system comprising: one or more processors; and a memory incommunication with the one or more processors and storing instructionsthat, when executed by the one or more processors, cause the one or moreprocessors to: receive, from a computing device, an indication of acurrent call being received at a current time from a particular phonenumber that is associated with a second user; utilize a trained calldifferentiation machine learning model that determines a likelihood thata particular call associated with a particular phone number is of afirst call type or a second call type, wherein the first call type isassociated with a first type of activity and the second call type isassociated with a second type of activity, wherein the trained calldifferentiation machine learning model is configured to: determine anactivity type associated with the second user, determining an activityqualifier associated with the second user based at least in part on theactivity type, and determine the likelihood that the current call is ofthe first call type or the second call type, based at least in part onthe current time and the activity qualifier; and instruct, when thecurrent call is of the first call type, the computing device of thefirst user to present to the first user, a graphical user interface(GUI) associated with the current call, wherein the GUI comprises atleast one GUI element, displaying, to the first user, informationrelated to the first type of activity of the second user.
 10. The systemof claim 9, wherein the trained call differentiating machine learningmodel has been trained based on: information of a first plurality ofusers, activity information associated with a first plurality ofactivities associated with the first plurality of users, phone numberinformation of a first plurality of phone numbers associated with afirst plurality of calls from the first plurality of users, the firstplurality of calls including calls of at least one of: the first calltype or the second call type, timing information of when the firstplurality of users engaging the first plurality of activities and/orinitiating the first plurality of calls, and at least one of: a) profileinformation of the first plurality of users; or b) contextualinformation associated with the first plurality of users.
 11. The systemof claim 9, wherein the information related to the first type ofactivity of the second user comprises at least one of: a title relatedto the first type of activity, an address related to the first type ofactivity, or a description of the first type of activities of the seconduser.
 12. The system of claim 9, wherein the call differentiationmachine learning model is retrained based on feedback data from thesecond user, the feedback data including at least an indication whetherthe call is of the first call type.
 13. The system of claim 9, whereinthe call is monitored to obtain feedback data with regard to whether thecall is of the first call type.
 14. The system of claim 9, wherein theone or more processors are further caused to: utilize the calldifferentiation machine learning model to determine an activity typeassociated with the first user; and determine the current call is of thefirst call type or the second call type, based at least in part on theactivity type associated with the first user.
 15. A non-transitorycomputer readable storage medium for tangibly storing computer programinstructions capable of being executed by a computer processor, thecomputer program instructions defining steps of: receiving, from acomputing device, an indication of a current call being received at acurrent time from a particular phone number that is associated with asecond user; utilizing a trained call differentiation machine learningmodel that determines a likelihood that a particular call associatedwith a particular phone number is of a first call type or a second calltype, wherein the first call type is associated with a first type ofactivity and the second call type is associated with a second type ofactivity, wherein the trained call differentiation machine learningmodel is configured to: determine an activity type associated with thesecond user, determining an activity qualifier associated with thesecond user based at least in part on the activity type, and determinethe likelihood that the current call is of the first call type or thesecond call type, based at least in part on the current time and theactivity qualifier; and instructing, when the current call is of thefirst call type, the computing device of the first user to present tothe first user, a graphical user interface (GUI) associated with thecurrent call, wherein the GUI comprises at least one GUI element,displaying, to the first user, information related to the first type ofactivity of the second user.
 16. The computer readable storage medium ofclaim 15, wherein the trained call differentiating machine learningmodel has been trained based on: information of a first plurality ofusers, activity information associated with a first plurality ofactivities associated with the first plurality of users, phone numberinformation of a first plurality of phone numbers associated with afirst plurality of calls from the first plurality of users, the firstplurality of calls including calls of at least one of: the first calltype or the second call type, timing information of when the firstplurality of users engaging the first plurality of activities and/orinitiating the first plurality of calls, and at least one of: a) profileinformation of the first plurality of users; or b) contextualinformation associated with the first plurality of users.
 17. Thecomputer readable storage medium of claim 15, wherein the informationrelated to the first type of activity of the second user comprises atleast one of: a title related to the first type of activity, an addressrelated to the first type of activity, or a description of the firsttype of activities of the second user.
 18. The computer readable storagemedium of claim 15, wherein the call differentiation machine learningmodel is retrained based on feedback data from the second user, thefeedback data including at least an indication whether the call is ofthe first call type.
 19. The computer readable storage medium of claim15, the steps further comprising: utilizing the call differentiationmachine learning model to determine an activity type associated with thefirst user; and determining the current call is of the first call typeor the second call type, based at least in part on the activity typeassociated with the first user.
 20. The computer readable storage mediumof claim 19, wherein the activity type associated with the first user isdetermined based at least in part on a transaction incurred by thesecond user with the first user.